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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.18

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/ampliseq analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2024-01-29, 12:50 CST based on data in: /home/gut/Documents/PQ00112/PAN/work/3c/57ad0d10efda4108cb7f866e28132d


        General Statistics

        Showing 234/234 rows and 3/6 columns.
        Sample Name% Dups% GCM Seqs
        Villapol_234_B_1
        93.0%
        55%
        0.0
        Villapol_234_B_2
        89.1%
        55%
        0.0
        Villapol_236_B_1
        92.7%
        54%
        0.0
        Villapol_236_B_2
        90.0%
        55%
        0.0
        Villapol_237_B_1
        92.3%
        55%
        0.0
        Villapol_237_B_2
        88.2%
        55%
        0.0
        Villapol_238_B_1
        92.4%
        55%
        0.0
        Villapol_238_B_2
        88.9%
        55%
        0.0
        Villapol_239_B_1
        92.7%
        56%
        0.1
        Villapol_239_B_2
        89.0%
        57%
        0.1
        Villapol_240_B_1
        92.6%
        55%
        0.0
        Villapol_240_B_2
        89.6%
        56%
        0.0
        Villapol_241_B_1
        91.7%
        55%
        0.0
        Villapol_241_B_2
        88.5%
        56%
        0.0
        Villapol_242_B_1
        92.4%
        55%
        0.0
        Villapol_242_B_2
        89.4%
        56%
        0.0
        Villapol_243_B_1
        92.2%
        55%
        0.1
        Villapol_243_B_2
        89.1%
        56%
        0.1
        Villapol_244_B_1
        93.1%
        56%
        0.1
        Villapol_244_B_2
        89.5%
        56%
        0.1
        Villapol_245_B_1
        93.1%
        55%
        0.1
        Villapol_245_B_2
        89.6%
        55%
        0.1
        Villapol_246_B_1
        91.7%
        55%
        0.1
        Villapol_246_B_2
        88.5%
        55%
        0.1
        Villapol_247_B_1
        92.6%
        56%
        0.1
        Villapol_247_B_2
        89.5%
        56%
        0.1
        Villapol_248_B_1
        91.9%
        56%
        0.1
        Villapol_248_B_2
        88.0%
        56%
        0.1
        Villapol_249_B_1
        93.1%
        54%
        0.1
        Villapol_249_B_2
        89.8%
        54%
        0.1
        Villapol_250_B_1
        92.8%
        55%
        0.1
        Villapol_250_B_2
        90.0%
        55%
        0.1
        Villapol_251_B_1
        93.4%
        53%
        0.1
        Villapol_251_B_2
        90.3%
        53%
        0.1
        Villapol_252_B_1
        92.2%
        55%
        0.1
        Villapol_252_B_2
        88.3%
        55%
        0.1
        Villapol_253_B_1
        92.9%
        54%
        0.1
        Villapol_253_B_2
        90.0%
        54%
        0.1
        Villapol_254_B_1
        93.0%
        55%
        0.0
        Villapol_254_B_2
        88.5%
        56%
        0.0
        Villapol_255_B_1
        92.6%
        55%
        0.1
        Villapol_255_B_2
        89.5%
        56%
        0.1
        Villapol_256_B_1
        92.2%
        55%
        0.0
        Villapol_256_B_2
        88.8%
        56%
        0.0
        Villapol_257_B_1
        92.3%
        55%
        0.1
        Villapol_257_B_2
        88.8%
        56%
        0.1
        Villapol_258_B_1
        91.5%
        55%
        0.0
        Villapol_258_B_2
        88.4%
        56%
        0.0
        Villapol_259_B_1
        93.1%
        55%
        0.1
        Villapol_259_B_2
        89.8%
        56%
        0.1
        Villapol_260_B_1
        92.2%
        56%
        0.1
        Villapol_260_B_2
        88.3%
        56%
        0.1
        Villapol_261_B_1
        93.3%
        55%
        0.1
        Villapol_261_B_2
        89.8%
        56%
        0.1
        Villapol_262_B_1
        92.1%
        56%
        0.0
        Villapol_262_B_2
        86.9%
        56%
        0.0
        Villapol_263_B_1
        93.9%
        55%
        0.0
        Villapol_263_B_2
        90.4%
        56%
        0.0
        Villapol_264_B_1
        93.2%
        56%
        0.1
        Villapol_264_B_2
        88.8%
        56%
        0.1
        Villapol_265_B_1
        93.6%
        55%
        0.1
        Villapol_265_B_2
        90.6%
        57%
        0.1
        Villapol_266_B_1
        93.3%
        56%
        0.0
        Villapol_266_B_2
        88.6%
        57%
        0.0
        Villapol_267_B_1
        94.8%
        56%
        0.1
        Villapol_267_B_2
        91.3%
        57%
        0.1
        Villapol_268_B_1
        93.1%
        56%
        0.0
        Villapol_268_B_2
        89.1%
        57%
        0.0
        Villapol_269_B_1
        93.4%
        56%
        0.1
        Villapol_269_B_2
        89.8%
        56%
        0.1
        Villapol_270_B_1
        93.1%
        56%
        0.1
        Villapol_270_B_2
        89.0%
        56%
        0.1
        Villapol_271_B_1
        92.0%
        56%
        0.0
        Villapol_271_B_2
        88.4%
        57%
        0.0
        Villapol_272_B_1
        92.7%
        55%
        0.0
        Villapol_272_B_2
        89.1%
        56%
        0.0
        Villapol_273_B_1
        92.1%
        56%
        0.1
        Villapol_273_B_2
        89.2%
        57%
        0.1
        Villapol_274_B_1
        91.4%
        54%
        0.1
        Villapol_274_B_2
        88.7%
        55%
        0.1
        Villapol_275_B_1
        92.6%
        55%
        0.1
        Villapol_275_B_2
        89.5%
        55%
        0.1
        Villapol_276_B_1
        91.8%
        55%
        0.1
        Villapol_276_B_2
        89.1%
        55%
        0.1
        Villapol_277_B_1
        92.7%
        54%
        0.1
        Villapol_277_B_2
        89.5%
        54%
        0.1
        Villapol_278_B_1
        92.3%
        54%
        0.1
        Villapol_278_B_2
        89.4%
        54%
        0.1
        Villapol_279_B_1
        90.8%
        56%
        0.0
        Villapol_279_B_2
        87.8%
        56%
        0.0
        Villapol_280_B_1
        92.9%
        56%
        0.1
        Villapol_280_B_2
        91.2%
        56%
        0.1
        Villapol_281_B_1
        92.4%
        56%
        0.1
        Villapol_281_B_2
        88.8%
        57%
        0.1
        Villapol_282_B_1
        93.5%
        56%
        0.0
        Villapol_282_B_2
        90.1%
        57%
        0.0
        Villapol_283_B_1
        92.6%
        56%
        0.1
        Villapol_283_B_2
        89.0%
        56%
        0.1
        Villapol_284_B_1
        92.2%
        56%
        0.1
        Villapol_284_B_2
        90.3%
        57%
        0.1
        Villapol_285_B_1
        92.5%
        56%
        0.1
        Villapol_285_B_2
        89.9%
        57%
        0.1
        Villapol_286_B_1
        92.1%
        55%
        0.1
        Villapol_286_B_2
        88.6%
        56%
        0.1
        Villapol_287_B_1
        93.3%
        55%
        0.1
        Villapol_287_B_2
        90.2%
        56%
        0.1
        Villapol_288_B_1
        91.2%
        56%
        0.1
        Villapol_288_B_2
        88.2%
        56%
        0.1
        Villapol_289_B_1
        93.9%
        56%
        0.1
        Villapol_289_B_2
        90.2%
        57%
        0.1
        Villapol_290_B_1
        94.3%
        55%
        0.0
        Villapol_290_B_2
        91.0%
        57%
        0.0
        Villapol_291_B_1
        93.9%
        56%
        0.0
        Villapol_291_B_2
        91.2%
        57%
        0.0
        Villapol_292_B_1
        93.2%
        55%
        0.1
        Villapol_292_B_2
        90.0%
        56%
        0.1
        Villapol_293_B_1
        94.6%
        56%
        0.1
        Villapol_293_B_2
        91.2%
        57%
        0.1
        Villapol_35d_234_1
        92.9%
        56%
        0.1
        Villapol_35d_234_2
        88.3%
        57%
        0.1
        Villapol_35d_236_1
        90.6%
        55%
        0.0
        Villapol_35d_236_2
        87.3%
        56%
        0.0
        Villapol_35d_237_1
        93.3%
        56%
        0.1
        Villapol_35d_237_2
        89.9%
        57%
        0.1
        Villapol_35d_238_1
        92.1%
        55%
        0.1
        Villapol_35d_238_2
        89.0%
        56%
        0.1
        Villapol_35d_239_1
        92.7%
        56%
        0.1
        Villapol_35d_239_2
        89.3%
        56%
        0.1
        Villapol_35d_240_1
        92.2%
        56%
        0.1
        Villapol_35d_240_2
        88.5%
        56%
        0.1
        Villapol_35d_241_1
        92.7%
        56%
        0.1
        Villapol_35d_241_2
        89.8%
        56%
        0.1
        Villapol_35d_242_1
        93.1%
        56%
        0.1
        Villapol_35d_242_2
        88.4%
        56%
        0.1
        Villapol_35d_243_1
        92.6%
        56%
        0.1
        Villapol_35d_243_2
        88.8%
        56%
        0.1
        Villapol_35d_244_1
        90.9%
        55%
        0.0
        Villapol_35d_244_2
        87.0%
        56%
        0.0
        Villapol_35d_245_1
        93.0%
        55%
        0.1
        Villapol_35d_245_2
        89.1%
        56%
        0.1
        Villapol_35d_246_1
        93.5%
        55%
        0.0
        Villapol_35d_246_2
        90.2%
        55%
        0.0
        Villapol_35d_247_1
        93.7%
        55%
        0.1
        Villapol_35d_247_2
        90.2%
        55%
        0.1
        Villapol_35d_248_1
        92.8%
        56%
        0.1
        Villapol_35d_248_2
        88.1%
        56%
        0.1
        Villapol_35d_249_1
        92.8%
        55%
        0.1
        Villapol_35d_249_2
        89.0%
        56%
        0.1
        Villapol_35d_250_1
        92.8%
        56%
        0.1
        Villapol_35d_250_2
        89.4%
        57%
        0.1
        Villapol_35d_251_1
        93.1%
        55%
        0.1
        Villapol_35d_251_2
        89.0%
        55%
        0.1
        Villapol_35d_252_1
        92.9%
        56%
        0.1
        Villapol_35d_252_2
        89.9%
        56%
        0.1
        Villapol_35d_253_1
        92.6%
        56%
        0.1
        Villapol_35d_253_2
        89.5%
        57%
        0.1
        Villapol_35d_254_1
        91.7%
        56%
        0.0
        Villapol_35d_254_2
        87.7%
        56%
        0.0
        Villapol_35d_255_1
        93.0%
        55%
        0.1
        Villapol_35d_255_2
        90.1%
        56%
        0.1
        Villapol_35d_256_1
        92.6%
        56%
        0.1
        Villapol_35d_256_2
        89.9%
        57%
        0.1
        Villapol_35d_257_1
        92.1%
        55%
        0.1
        Villapol_35d_257_2
        88.8%
        56%
        0.1
        Villapol_35d_258_1
        93.0%
        56%
        0.0
        Villapol_35d_258_2
        89.8%
        57%
        0.0
        Villapol_35d_259_1
        93.5%
        56%
        0.1
        Villapol_35d_259_2
        90.0%
        57%
        0.1
        Villapol_35d_261_1
        92.5%
        56%
        0.1
        Villapol_35d_261_2
        88.2%
        57%
        0.1
        Villapol_35d_262_1
        94.3%
        56%
        0.0
        Villapol_35d_262_2
        90.6%
        58%
        0.0
        Villapol_35d_263_1
        93.9%
        56%
        0.1
        Villapol_35d_263_2
        90.5%
        57%
        0.1
        Villapol_35d_264_1
        93.1%
        57%
        0.1
        Villapol_35d_264_2
        90.0%
        57%
        0.1
        Villapol_35d_265_1
        93.0%
        57%
        0.1
        Villapol_35d_265_2
        89.4%
        57%
        0.1
        Villapol_35d_266_1
        93.3%
        57%
        0.1
        Villapol_35d_266_2
        89.6%
        57%
        0.1
        Villapol_35d_267_1
        94.0%
        55%
        0.1
        Villapol_35d_267_2
        90.2%
        55%
        0.1
        Villapol_35d_268_1
        90.8%
        56%
        0.0
        Villapol_35d_268_2
        87.8%
        57%
        0.0
        Villapol_35d_269_1
        94.1%
        56%
        0.0
        Villapol_35d_269_2
        90.8%
        57%
        0.0
        Villapol_35d_270_1
        93.2%
        57%
        0.1
        Villapol_35d_270_2
        89.5%
        57%
        0.1
        Villapol_35d_271_1
        93.7%
        56%
        0.1
        Villapol_35d_271_2
        90.2%
        57%
        0.1
        Villapol_35d_272_1
        93.3%
        56%
        0.0
        Villapol_35d_272_2
        89.3%
        57%
        0.0
        Villapol_35d_273_1
        93.5%
        57%
        0.0
        Villapol_35d_273_2
        88.8%
        57%
        0.0
        Villapol_35d_274_1
        92.7%
        55%
        0.1
        Villapol_35d_274_2
        89.4%
        56%
        0.1
        Villapol_35d_275_1
        93.0%
        56%
        0.1
        Villapol_35d_275_2
        89.1%
        57%
        0.1
        Villapol_35d_276_1
        92.0%
        55%
        0.1
        Villapol_35d_276_2
        88.1%
        56%
        0.1
        Villapol_35d_277_1
        92.1%
        56%
        0.1
        Villapol_35d_277_2
        89.1%
        57%
        0.1
        Villapol_35d_278_1
        92.7%
        56%
        0.1
        Villapol_35d_278_2
        89.5%
        57%
        0.1
        Villapol_35d_279_1
        92.8%
        55%
        0.1
        Villapol_35d_279_2
        89.5%
        56%
        0.1
        Villapol_35d_280_1
        92.7%
        56%
        0.1
        Villapol_35d_280_2
        89.8%
        57%
        0.1
        Villapol_35d_281_1
        92.4%
        56%
        0.1
        Villapol_35d_281_2
        89.7%
        56%
        0.1
        Villapol_35d_282_1
        91.8%
        56%
        0.1
        Villapol_35d_282_2
        88.5%
        57%
        0.1
        Villapol_35d_283_1
        90.5%
        56%
        0.0
        Villapol_35d_283_2
        86.4%
        57%
        0.0
        Villapol_35d_284_1
        94.0%
        56%
        0.1
        Villapol_35d_284_2
        91.0%
        57%
        0.1
        Villapol_35d_285_1
        93.3%
        55%
        0.0
        Villapol_35d_285_2
        89.1%
        56%
        0.0
        Villapol_35d_286_1
        94.1%
        56%
        0.1
        Villapol_35d_286_2
        90.8%
        57%
        0.1
        Villapol_35d_287_1
        93.5%
        55%
        0.1
        Villapol_35d_287_2
        90.9%
        56%
        0.1
        Villapol_35d_288_1
        92.2%
        56%
        0.1
        Villapol_35d_288_2
        88.7%
        57%
        0.1
        Villapol_35d_289_1
        93.1%
        55%
        0.1
        Villapol_35d_289_2
        90.1%
        56%
        0.1
        Villapol_35d_290_1
        91.8%
        55%
        0.0
        Villapol_35d_290_2
        88.8%
        56%
        0.0
        Villapol_35d_291_1
        93.8%
        56%
        0.1
        Villapol_35d_291_2
        89.5%
        57%
        0.1
        Villapol_35d_292_1
        91.9%
        56%
        0.1
        Villapol_35d_292_2
        87.8%
        56%
        0.1
        Villapol_35d_293_1
        94.5%
        56%
        0.1
        Villapol_35d_293_2
        91.5%
        58%
        0.1

        FastQC

        Version: 0.12.1

        FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Sequence Length Distribution

        All samples have sequences of a single length (300bp).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceSamplesOccurrences% of all reads
        GATGAACGCTAGCGACAGGCTTAACACATGCAAGTCGAGGGGCAGCGGGG
        117
        382390
        3.0080%
        GATGAACGCTGGCGGCGTGCCTAATACATGCAAGTCGAGCGAACCACTTC
        117
        255097
        2.0067%
        GATGAACGCTAGCGGCAGGCTTAACACATGCAAGTCGAGGGGCATCGGGA
        117
        172192
        1.3545%
        GATGAACGCTAGCGGCAGGCTTAACACATGCAAGTCGAGGGGCAGCGAGA
        117
        165564
        1.3024%
        GATGAACGCTAGCTACAGGCTTAACACATGCAAGTCGAGGGGCAGCATTT
        117
        110024
        0.8655%
        GACGAACGCTGGCGGCGTGCTTAACACATGCAAGTCGAACGGAGCACCCC
        117
        27308
        0.2148%
        CACGGAGTTAGCCGATGCTTTTTCTCCGGGTACTCTCGACGCGCGTTCAC
        117
        309896
        2.4377%
        CACGGAGTTAGCCGATGCTTTTTCTTCGGATACACGCAGTCCGGGACACG
        117
        187459
        1.4746%
        CACGTAGTTAGCCGTGGCTTTCTCATAAGGTACCGTCAATTGATAGTCAT
        117
        242164
        1.9049%
        CACGTAGTTAGCCGGGGCTTCTTAGTCAGGTACCGTCATTTTCTTCCCTG
        117
        255038
        2.0062%
        CACGGAGTTAGCCGATCCTTATTCGTACGATACTTTCAGACAGATACGCG
        117
        163445
        1.2857%
        CACGGAGTTAGCCGATCCTTATTCATATGGTACATACAAAATTCCACACG
        117
        104420
        0.8214%
        CACGTAGTTAGCCGTGGCTTATTCGACAGGTACCGTCTTCTGCTCTTCCC
        117
        41068
        0.3231%
        GACGAACGCTGGCGGCGTGCCTAATACATGCAAGTCGAGCGAGCTTGCCT
        116
        543271
        4.2735%
        GATGAACGCTGGCGGCGTGCTTAATACATGCAAGTCGAACGAAGCACCTC
        116
        83290
        0.6552%
        GACGAACGCTGGCGGCGTGCTTAACACATGCAAGTCGAACGGAGTACCCT
        116
        40717
        0.3203%
        CACGTAGTTAGCCGTGACTTTCTAAGTAATTACCGTCAAATAAAGGCCAG
        116
        523940
        4.1215%
        CACGTATTTAGCCGGGGCTTCTTAGTCAAGTACCGTCATTTTCTTCCTTG
        116
        84001
        0.6608%
        CACGTAGTTAGCCGTGGCTTATTCCTCAGGTACCGTCACTTGCTTCGTCC
        115
        21419
        0.1685%
        CACGTAGTTAGCCGTGGCTTATTCGTCAGGTACCGTCACTTGCTTCGTCC
        115
        25531
        0.2008%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

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        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

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        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        FastQC0.12.1

        nf-core/ampliseq Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.

        Methods

        Data was processed using nf-core/ampliseq v2.8.0 (doi: 10.5281/zenodo.1493841) (Straub et al., 2020) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v23.10.1 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/ampliseq -r 2.8.0 --input Samplesheet.tsv --metadata PAN_Metadata.tsv --outdir PAN_ampliseq -profile singularity --picrust --metadata_category Sex,Injury,Treatment,Timepoint --FW_primer AGAGTTTGATYMTGGCTCAG --RV_primer ATTACCGCGGCKGCTGG --trunclenf 275 --trunclenr 265 --skip_cutadapt --max_cpus 12 -resume

        References

        • Straub D, Blackwell N, Langarica-Fuentes A, Peltzer A, Nahnsen S, Kleindienst S. Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline. Front Microbiol. 2020 Oct 23;11:550420. https://doi.org/10.3389/fmicb.2020.550420
        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/ampliseq Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        BARRNAP barrnap 0.9
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.12.0
        yaml 6.0.1
        DADA2_DENOISING R 4.3.1
        dada2 1.28.0
        DADA2_FILTNTRIM R 4.3.1
        dada2 1.28.0
        DADA2_QUALITY1 R 4.3.1
        ShortRead 1.58.0
        dada2 1.28.0
        DADA2_TAXONOMY R 4.3.1
        dada2 1.28.0
        FASTQC fastqc 0.12.1
        FILTER_STATS pandas 1.1.5
        python 3.9.1
        PHYLOSEQ R 4.3.0
        phyloseq 1.44.0
        PICRUST picrust2 PICRUSt2 2.5.2
        python 3.8.16
        RENAME_RAW_DATA_FILES sed 4.7
        Workflow Nextflow 23.10.1
        nf-core/ampliseq 2.8.0

        nf-core/ampliseq Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        revision
        2.8.0
        runName
        happy_rubens
        containerEngine
        singularity
        launchDir
        /home/gut/Documents/PQ00112/PAN
        workDir
        /home/gut/Documents/PQ00112/PAN/work
        projectDir
        /home/gut/.nextflow/assets/nf-core/ampliseq
        userName
        gut
        profile
        singularity
        configFiles
        N/A

        Main arguments

        input
        Samplesheet.tsv
        FW_primer
        AGAGTTTGATYMTGGCTCAG
        RV_primer
        ATTACCGCGGCKGCTGG
        metadata
        PAN_Metadata.tsv
        outdir
        PAN_ampliseq

        Read trimming and quality filtering

        trunclenf
        275
        trunclenr
        265

        Downstream analysis

        metadata_category
        Sex,Injury,Treatment,Timepoint
        picrust
        true

        Skipping specific steps

        skip_cutadapt
        true

        Max job request options

        max_cpus
        12